Staff ML Engineer
Tasks
- Build and scale ML platform for fast production deployment
- Design ML architecture for feature engineering training evaluation and serving
- Design data pipelines for code change signals and test history
- Extract features from code diffs for generalised test selection
- Instrument observability metrics for ML predictions accuracy latency coverage drift
- Investigate and resolve performance and reliability issues across data and ML stack
- Lead ML strategy for predictive test selection
- Mentor engineers and share best practices via documentation and pairing
- Solve cold start problem for new tests and repositories
- Support cross team tooling infrastructure and frameworks
- Train evaluate and deploy prediction models with monitoring and retraining
Perks/Benefits
Skills/Tech-stack
AWS | Anomaly Detection | Apache Flink | Apache Spark | Batch Processing | Change Point | Change point detection | Classification | Cold Start | Container Orchestration | Data Pipelines | Docker | Drift Detection | Experiment tracking | Feature Engineering | Feature Store | Go | GraphQL | Kubernetes | Latency optimization | Machine Learning | Model Deployment | Model Evaluation | Model Monitoring | Model Registry | Model Training | Observability | Prediction Accuracy | Python | Ranking | React | Recommendation Systems | Ruby on Rails | SQL | Streaming processing
Education
N/A
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